MCGS-SLAM: A Multi-Camera SLAM Framework Using Gaussian Splatting for High-Fidelity Mapping

Figure 1: MCGS-SLAM synchronizes RGB inputs from the front, left, and right cameras of the multi-camera rig in the Waymo dataset and fuses them into a unified 3D Gaussian Splatting map. The system performs real-time tracking and mapping, enabling high-fidelity reconstruction of both color and depth views from each individual camera. Through joint multi-camera optimization, MCGS-SLAM ensures accurate pose and geometry alignment, while supporting comprehensive multi-view rendering for photorealistic visualization. Abstract-- Recent progress in dense SLAM has primarily targeted monocular setups, often at the expense of robustness and geometric coverage. We present MCGS-SLAM, the first purely RGB-based multi-camera SLAM system built on 3D Gaussian Splatting (3DGS). A multi-camera bundle adjustment (MCBA) jointly refines poses and depths via dense photometric and geometric residuals, while a scale consistency module enforces metric alignment across views using low-rank priors. The system supports RGB input and maintains real-time performance at large scale.